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1
STOCHASTIC ANALYSIS OF A GAS TURBINE SYSTEM WITH PRIORITY AND RANDOM INSPECTION BY SINGLE SERVER UNDER DIFFERENT HUMID
CONDITIONS
Pinki, Vijeta Kumari and Dalip Singh
Department of Mathematics, Maharshi Dayanand University Rohtak, India pinkichahar95@gmail.com, vijetadahiya1993@gmail.com and dsmdur@gmail.com
Abstract
In this study, we investigated the impact of two different humid levels on the reliability measures of a stochastic model for a gas turbine system composed of a gas turbine and a steam turbine. To enhance the system's overall performance, we prioritize gas turbine repair over steam turbine repair in addition to a combined inspection and preventative maintenance approach. To find some reliability measures, such as the mean time to system failure, availability, etc., semi-Markov process and regenerating point technique are utilized. These measures are analysed graphically based on the data obtained from a gas turbine power plant in Delhi, India.
Keywords: Gas Turbine, Steam Turbine; Reliability; Cost-Benefit; Maintenance;
1. Introduction
The growing global demand for electrical energy, driven by factors such as rapid industrialization, urbanization and the increasing number of electronic gadgets has put significant pressure on our existing power generation systems. Researchers from all around the world have conducted substantial research into the complex dynamics of energy supply, demand, and security in both developed and emerging economies [1-3]. The nation of India, which has to cope with its own unique issues and potential is the focus of in-depth research on electricity demand [4]. The study made by Zhang et al. goes beyond typical clustering algorithms to investigate novel approaches to analyse electricity usage trends [5]. Optimizing demand response initiatives within smart grids is studied by Derakhshan et al. using TBLO and SFL algorithms [6]. Furthermore, [7] points out the impact of these demand patterns on both power system costs and supply sufficiency. These research articles provide a comprehensive understanding of the complicated relationship between power demand and supply, as well as the issues created by shifting consumption patterns.
To address these issues, researchers and policymakers need to design and improve power generation systems that can satisfy the rising energy demands profitably and ensuring sustainability. Though, there are various ways to generate electricity, such as using water, sunlight, thermal energy from sources like coal, and harnessing nuclear reactions, leading to different types of power plants. In today's competitive energy markets, a new approach called the "Risk-Based Approach" is gaining attention for managing Virtual Power Plants. This approach is all about figuring out smart and efficient ways to schedule the activities of these
virtual power plants [8]. Operating power generating systems in demanding environments indeed presents several challenges [9]. The performance of a system inevitably deteriorates when operated over lengthy periods of time under adverse conditions. When this degradation exceeds a certain threshold, it may lead components or subsystems to fail, compromising the overall safety of the system. As a result, one of the key objectives of engineering systems is to provide timely maintenance. Preventive maintenance and corrective maintenance are two essential approaches for maintaining and managing equipment and systems in various industries [10]. The primary objective of any maintenance strategy is to uphold the system functionality to the greatest extent possible while striking a balance between downtime and maintenance expenses, thereby avoiding catastrophic breakdowns. Zaho et al. studied the preventive maintenance scheduling on gas turbine power plant through a sequential approach [11].
The key objective of this research paper is to develop a comprehensive operational stochastic model for a combined cycle power plant. Gas turbines, a crucial part of combined cycle power plant, are responsible for this decision since they have amazing qualities including great efficiency, adaptability, and quick start-up times. The significance of our research lies in addressing a previously unexplored aspect of the literature. Although there is a lot of information on the reliability of combined cycle power plants, none of the existing studies have considered the impact of humidity with priority and random inspection within a stochastic model using the semi-Markov approach. Recognizing the importance of humidity in power plant management, we have addressed a research gap by incorporating this critical aspect into our analysis.
Statistical methods play a fundamental role in the development of reliability/stochastic models, offering valuable insights that can inform maintenance and repair planning for technical systems. These reliability criteria are used to measure the system's potential for maintenance and repair. Table 1 provides a comprehensive summary of the foundational statistical techniques employed during the creation of reliability/stochastic models for various gas turbine and combined cycle power plants within the domains of the energy sector in recent years. Many researchers in Table 1 studied the reliability models for gas turbine systems under different conditions using different methods but none of the existing studies have considered the impact of humidity with priority and random inspection within a stochastic model using the semi-Markov approach. Reliability models assist in figuring out how reliable a system is, how frequently it may fail, and how quickly it may recover from those failures. Creating such models for gas turbine power plants allows engineers and researchers to foresee future faults, develop effective maintenance procedures, and maximize the overall performance and operating efficiency of these systems.
This study aims to conduct a thorough investigation into the effects of two distinct humidity levels (i.e., humidity less than or equal to 50% and humidity greater than 50%) on the reliability measures of a stochastic model for a gas turbine system. The system comprises a gas turbine and a steam turbine and has been developed under specific assumptions that have not been addressed in the existing literature. Through a comprehensive investigation into the impact of humidity variations on the reliability measures of gas turbine systems, we aim to deepen our understanding and provide invaluable insights in this field. Thus it will provide a comprehensive analysis of our research methodology, the experimental setup, data collection, and, ultimately, the results and implications of our findings. By doing so, we aim to promote the efficient and reliable utilization of gas turbine systems, thus furthering the cause of sustainable energy generation and contributing to the broader goals of the industry.
Table 1: Summary of literature review in recent years
Methods Model Structure Characteristic of Study References
Mathematical modeling Impact of ambient conditions on CCPP in Syria [12]
Statistical Methods AI-coherent modeling Data-driven forecasting for a CCPP using BFGS algorithm [13]
Thermodynamic modelling Effect of temperature and relative humidity on gas turbine [14]
Mathematical modeling Effects of the intake air humidity on the gas turbine [15]
Monte Carlo and MLE method Stochastic modeling CCPP study under temperature fluctuations in Tehran [16]
PJM method Four-state reliability model Combined heat and power plants [17]
Reliability modeling CCPP with schedule inspection [18]
Semi-Markov Process and RGT Reliability modeling CCPP with random inspection [19]
Three-unit CCPP reliability modeling Effect of ambient temperature with FCFS repair pattern on CCPP [20]
Reliability modeling Effect of humidity on CCPP in Delhi [21]
2. Modeling of System
In this paper, we discuss the impact of two different humid levels (i.e., humidity less than or equal to 50% and humidity greater than 50%) on the reliability measures of a stochastic model for a gas turbine system composed of a gas turbine and a steam turbine. To enhance the system's overall performance, we prioritize gas turbine repair over steam turbine repair in addition to a combined inspection and preventative maintenance approach as shown in Figure 1. To find some reliability measures, such as the mean time to system failure, availability, etc., we employ the semi-Markov process and regenerating point technique, which are well-suited for this type of analysis. At initial stage, both units, the gas turbine and the steam turbine are up and completely operational, operating together in a combined cycle. Steam turbine failure keeps the system in upstate mode with partially working and termed as single cycle. However, if the gas turbine fails, the system transitions to a downstate mode.
The following reasonable assumptions are used to create the model:
• The failure time distribution is presumed to be exponential, whereas the repair/maintenance time distribution is arbitrary.
• After each maintenance/repair activity, the unit is stated to be as satisfactory as new.
• The system's repair sequence adheres to a first come, first serve basis, except in cases of complete system failure, where priority is given to gas turbine repair over steam turbine repair.
• System failure is asserted when both units fail.
• Regenerative Point
Figure 1: State Transition Diagram of the System
Description of the states in Figure 1:
S0/S1 : Both units are operational when humidity is < 50% / > 50%.
S2/S3 : System is down due to inspection when humidity is < 50% / > 50%.
S4/S5 : System is operational with the gas turbine running and the steam turbine under repair when humidity is < 50% / > 50%.
S6/S7 : System is down with the gas turbine under repair and the steam turbine also down when humidity is < 50% / > 50%.
S8/S9 : System has failed, with the gas turbine under repair and the steam turbine awaiting repair when humidity is < 50% / > 50%.
S10/S12 : System is operational with the gas turbine running and the steam turbine under maintenance when humidity is < 50% / > 50%.
S11/S13 : System is down with the gas turbine under repair and the steam turbine also down when humidity is < 50% / > 50%.
2.1 Notations
01/02 : rate of gas turbine failure when humidity is </> 50%.
: rate of steam turbine failure when humidity is </> 50%. BH1 (t)/BH2 (t) : server is busy at a particular time t when the humidity is </> 50%. DH1 (t)/DHj2 (t) : system is in a down state at specific time t when the humidity is </> 50%. h1 (t)/H1 (t) : pdf/cdf of time changing humidity from < 50% to > 50%.
Pinki, Vijeta Kumari and Dalip Singh RT&A, No 3 (79) STOCHASTIC ANALYSIS OF A GAS TURBINE SYSTEM_Volume 19, September 2024
h2 (t)/H2 (t) : pdf/cdf of time changing humidity from > 50% to < 50%.
i(t)/I(t) : pdf/cdf of the examination to identify the type of maintenance required.
IH1 (t)/IHj2 (t) : system is under inspection at a particular time t when the humidity is </> 50%.
m1 (t)/M1 (t) : pdf/cdf of gas turbine maintenance.
m2 (t)/M2 (t) : pdf/cdf of steam turbine maintenance.
p1/p2 : probability that an inspection will indicate the need for gas turbine maintenance
when humidity is </> 50%.
p2/p2 : probability that an inspection will indicate the need for steam turbine maintenance
when humidity is </> 50%.
qij (t)/Qjj (t) : pdf/cdf of the first-passage time without visiting any other regenerative state from regenerative state i to a regenerative state j or a failed state j in (0, t).
q(k)/Q(k)(t) : pdf/cdf of first-passage time from regenerative state i to a regenerative state j, visiting state k one time in (0, t]
r1 (t)/R1 (t) : time for gas turbine repair in pdf/cdf respectively. r2 (t)/R2 (t) : time for steam turbine repair in pdf/cdf respectively. ui (t)/Ui (t) : time required to inspect the system in pdf/cdf. VHi1(t)/VHi2(t) : server's expected number of visits when humidity is </> 50%. ©/© : Laplace convolution/ Laplace Stieltjes convolution
3. State Transition Probabilities and Mean Sojourn Time
The expression dQij (t) for all essential combinations of i and j is generated based on state transition diagram and the transition probabilities p^ are computed by applying Laplace transform and
utilizing p.j= limqij(s) .
Table 2: State Transition Probabilities
dQ01 = e-(0i+^i)tF1 dQo6 = 0ie-(9l+^l)tF4 dQis = ^e-^+^Fs
dQ2,ii = p|i(t)
dQ4o = e-9l(t)r2(t) dQs9 = 02e-92(t)R2(^^ dQ84 = ri(t) dQii,o = mi(t)
dQo2 = e-(9l+^l)tF2
dQio = e-(02+^2)tFo dQi7 = 02e-(02+^2)tF5 dQ3,i2 = p2i(t) dQ48 = 0ie-9l(t)R^
dQ6o = ri(t)
dQ95 = ri(t) dQi2,i = m2(t)
dQo4 = Aie-(9l+Xl)tF4 dQi3 = e-(02+^2)tF3,
dQ2,io = pii(t)
dQ3,i3 = p2i(t)
dQs
= e-02(t)
r2(t)
dQ7i = ri(t) dQio,o = m2(t)
dQi3,i = mi(t)
where, Fo = ^(0^(0, F1 = ^№(0, F2 = ^(0^(0, F3 = ^(0^(0
F4 = ^mm, = //,(//¿/,(0
Mean Sojourn Time (^) is the time the system expects to spend in state i. The expressions for ^ are produced by using = J0~P[Ti>t]dt where Ti denotes the system's stay time in state i.
Table 3: Mean Sojourn Time
Mo = +
M4= ^"[l-r^fli)]
^ = F5*(02 + À2)
^ = ^-[i-r2(e2)]
°2
/>œ />œ
= I M2(t) d = = I Mi (f) d = ^13 _¿0_¿0_
f OO
= I Kt)dt=
Jo
= I Rl(t)dt= Jo
4. Reliability Measures
4.1 Mean Time to System Failure (MTSF)
Assuming that $ (t) represents the cumulative distribution function of the initial-passage time from a failed state to a regenerative state i. The recursive relations listed below are employed to compute the system's mean time to failure.
$0(t) = Q0l(t)®$l(t) + Qo2(t)®$2(t) + Q„4(t)®$4(t) + Q06(t)®$6(t) (1)
$l(t) = Qlo(t)®^o(t) + Ql3(t)®^3(t) + Ql5(t)®^s(t) + Ql7(t)®$7(t) (2)
^z(t) = Q2,lo(t)®^lo(t) + Q2,ll(t)®^ll(t) (3)
$3 (t) = Q3,l2 0)®$l2 (t) + Q3,l3 0)®$l3 (t) (4)
$4 (t) = Q4o (t)®$o (t) + Q48 (t) (5)
$5(t) = Q5l(t)®$l(t)+Q59(t) (6)
$6(t) = Q6o(t)®$o(t) (7)
$7(t) = Q7l(t)®$l(t) (8)
$lo(t) = Qlo,o(t)®$o(t) (9)
$ll(t) = Qll,o(t)®$o(t) (10)
$l2(t) = Ql2,l(t)®$l(t) (11)
$l3(t) = Ql3,l(t)®$l(t) (12)
Using Laplace Stieltjes Transform on both sides of aforementioned relations and Cramer's Rule to solve them, we get
H0V) _N
MTSF = lim
s^0 s
(13)
where, N = (Pio + PlsP59)(^0 + Po2fe + P04M + PoiPl3^3 + Poi^i + Pi5^s(Poi + P04P48) D = PlsP59(Poi + P04P48) + P04P10P48
D
4.2 Steady State Availability
and indicates how likely it is that the system will be in a combined
cycle or single cycle at any given time t, assuming that it was in a regenerative condition at time t=0 when the humidity is < and > 50%. We obtain the equations for availability in both combined and single cycles by studying empirical argumentation and solving the resulting equations by using the Laplace Transform, we get
V =
1 -p01 - p02 0 P04 0 -p06 0 0 0 0 0 0 0
-P10 1 0 -p13 0 P15 0- p17 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0- p2,10 -p2,11 0 0
0 0 0 1 0 0 0 0 0 0 0 0 -p3,12 -p3,13
-p40 0 0 0 1 0 0 0 -p48 0 0 0 0 0
0 -P51 0 0 0 1 0 0 0 P59 0 0 0 0
-p60 0 0 0 0 0 1 0 0 0 0 0 0 0
0 -p71 0 0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 -p84 0 0 0 1 0 0 0 0 0
0 0 0 0 0 P95 0 0 0 1 0 0 0 0
-p10,0 0 0 0 0 0 0 0 0 0 1 0 0 0
-p11,0 0 0 0 0 0 0 0 0 0 0 1 0 0
0- p12,1 0 0 0 0 0 0 0 0 0 0 1 0
0- p13,1 0 0 0 0 0 0 0 0 0 0 0 1
C2 C3 C4 C5 Q C7 Q C9 C10 C11 ^12 C13 C14I
Or V1 = |Ci
where Q (1 < i < 14), represents the ith column of the V^. i/i — | C*i Cy C^ C ^ C^ CQ Cm c
2 u3 u4 u5 u6 u7 00000000
Ci11 = [ft
(t) = —, where V = 71
J8 u9 u10 0000
11 0]
C13 C141
V
Similarly, 4tf2(t) = where, Ü,- = |C"J'
Cf2 = [0
Ü2
^(0 =
C7 C*8
£4
V
Cq C-
10
y 0 v y y
■] = C2 C3 C4 C5 C6
and (2 < j < 4)
n2 - rn ^ 00000000000 0] 0 0 0 m4 0 0 0 0 0 M10 0 0 0]
^12 C13 C141
Cf3
= [0
Ci14 = [0 0 0 0 0 Ms 0 0 0 0 0 0 /x10 0]
4.3 Other Performance Measures
DffoHt) = f, D//2(0 = £ , WoHt) = f, = £, = £, B//2(0 = ^
where, (5 < j < 12)
=| ¿T C2 C3 C4 C5 C6 C7 C9 C10 C11 C12 C13 C14
Cf5 = [0 0 M2 0 0 0 M6 0 0 0 0 M11 0 0]
n 1 s 6 = 0 0 0 M3 0 0 0 M6 0 0 0 0 0 M11]
Cf7 = [0 0 M2 0 0 0 0 0 0 0 0 0 0 0]
Ci18 = [0 0 0 M2 0 0 0 0 0 0 0 0 0 0]
Ci19 = [0 0 00 M4 0 M6 0 0 0 M10 M11 0 0]
Ci110 = [0 0 00 0 Ms 0 M6 0 0 0 0 M10 M11]
Ci11 = [P02 + P04 + P06 0 0 M2 000000000 0]
(14)
(15)
= [0 p13 + p15 + p17 0 0 0 0 0 0 0 0 0 0 0 0]
4.4 Profit of the System
p = c1 * ah1 + c2 * ah2 + c3 * ah1s + c4 * ah2s - c5 * bh0 - c6 * bh2 - c7 * vh1 - c8 * vhg - pe
C1/C2 : Revenue earned per unit when system works in combined cycle for humidity </> 50% C3/C4 : Revenue earned per unit when system works in single cycle for humidity </> 50% C5/C6 : Expense per unit time when server is busy for humidity </> 50% C7/C8 : Cost per visit by server when humidity is </> 50% PE : Additional expenses of Plant
C
12
Cf12
5. Results and Discussion
For numerical calculations, we study the specific situation, in which all temporal distributions are assumed to be exponential which are best fitted over real time data, as established by Singh [22]. We examined one-year real-time data from a gas turbine power plant in Delhi, India, restricted the temperature range to up to 25°C, and the methodology used to obtain the values of all the parameters is provided in appendix, which are used to assess the graphical behaviour of reliability measures. The following distributions have been assumed for various times.
¿(t) = ye-r(t), r1(t) = a1e
"i(t), r2(t) = a2e-a2(t), mi(t) = ae-a(t), m2(t) = 2e-^M, u.(t) = 0e-eM
5.1 MTSF V/s Failure rate A1 for different values of 02
Figure 2 illustrate the behaviour of mean time to system failure v/s failure rate of steam turbine A1for different values of d2. MTSF decreases with increase in any one of the failure rate Alr 0,, A2and d2.
>.2=0.00203
>.2=0.00303
40000 35000 30000 25000 {/) 20000 t 15000 =- 10000 2 5000 40000 re 35000
Li.
30000 § 25000 % 20000 ^ 15000 0 10000 5000
0J
£ 40000 ¡Z 35000 = 30000 ™ 25000 g 20DDD 15000 10000 5000
°°a7 °o02 °0Os °co°o07 °.00J °Oo3 °o0< °Oos °o0°o0r o.00f o.0g3 o00t) o0os oOQe
Failure rate of steam turbine when humidity is < 50% (Ju,)
Figure 2: MTSF Vs Failure rates 81, 82,^1,^2
5.2 Availability in Steady State
Figure 3 demonstrates the availability in combined cycle when humidity is < 50% and when
humidity is > 50%
• Both availabilities (when humidity is </> 50%) of combined cycle decreases as we increase any one of the failure rates.
• Availability in combined cycle when humidity is > 50% is higher than availability in combined cycle when humidity is < 50%.
Figure 4 demonstrates the availability in single cycle when humidity is < 50% and when humidity is
> 50%
• Availability when humidity is < 50%/> 50% of single cycle increases with increase in failure rate A1/A2 respectively.
Availability in single cycle (when humidity is > 50%) decreases smoothly with increase in failure rate X,.
avc1 , 02=0.00103
avco , ez=o.ooio3
avco , 82=0.00303 -
avci , e2=0.00303
0 in
■A
vi tn >1
1
i.2=0.00103
a^O.00203
0.7 0.6 0.5 0.4 0.3 0.2
b 0.7
® < 0.6 0.5
° > 04 TJ <
£ - 0.3
£ at
^it.ljlTL -■j---i---j---i"-
.i.i.J.
—i—!—I—
.L.
1 _
_.L.l.J. J._I_
: 1 1 .. : .. 1 1 ..
T'-.'c: ill
A
E o o
c
SS
ra >
<
0.2
0.7 0.6 0.5 0.4 0.3 0.2
1 _ j_
...i...
— -j—h—j——j —j— ^ —— |— j-— —--j—i——I- —
-T-i_ !l. I'.^l.iTX.J"--i-■ i■ i■ "i".4li 1"
1----1 —r —I— 1—1—----i —I----i— I — 1----I—i—i—i—i— —-j—I—>—j----
...L
-i
4
-tt ■
j __!_;_•_ _ i —^—i—^ - ^ —— i———■ J— U X. i.'jr J. Ull'l-i - -i- - i- - 4-
—;—h —I— 1— i—I— -—I — i—i
........' . T-
CM
o
o II
CM
ev o o o II
PJ
to o
V % % °o0°o07 0-o03 o.o0s o.0oo00j o.% o,% o.%
Failure rate of steam turbine when humidity is £ 50% (>_1) Figure 3: Availability in Combined Cycle Vs Failure rates dy 02, andX2
Failure rate of steam turbine when humidity is < 50% (J^) Figure 4: Availability in Single Cycle Vs Failure rates 0 02, and A2
5.3 Profit V/s Plant Expenses (PE) for different values of Price of Electricity (P)
— P = 3 INR — P = 3.5 INR—1— P = 4 INR
«,=0.0317 «2=0.0517
4000002000000
£ -200000 CD
ti -400000
CO -600000 <D
Plant Expenses (PE)
Figure 5: Profit Vs Plant Expenses for different P Values
Figure 5 illustrates the behavior of Profit of plant with respect to Plant Expenses.
• Profit increases with decrease in plant expenses.
• Profit increases with increase in values of P.
Table 4: Cut-off Values of PE for different values of a and a2
Price Per Unit «2 = 0.0317 «2 = 0.0517
ai = 0.0317 «1 = 0.0517 «1 = 0.0317 «1 = 0.0517
P=3 INR 839173.20 842787.19 849834.18 853495.72
P=3.5 INR 979100.50 983291.64 991540.49 995786.31
P=4 INR 1119027.81 1123796.09 1133246.81 1138076.91
Table 4 shows threshold points of plant expenses at particular price of electricity to achieve profit.
6. Conclusion
For two different humidity conditions (i.e., humidity less than or equal to 50% and humidity greater than 50%), a stochastic model of a gas turbine system composed of one gas turbine and one steam turbine is developed by prioritizing repair of gas turbine over steam turbine and applying random inspection and maintenance policy of a system using single service facility. Various reliability measures like system's mean time to failure, availability for steady state, etc. have been obtained and the graphical analysis of the effects of failure rates of steam turbines when humidity is </> 50%. Finding shows that mean time to system failure declines as failure rate increases. Trends in
availability for both cycles and varied humidity levels, i.e. when humidity is </> 50%, have been depicted with respect to steam turbine failure rate, and many interesting results about availability have been found. Profit for the plant is shown, which declines as the price of electricity decreases. Furthermore, a thorough study of gas turbine systems may be beneficial to people involved in the industry of electricity generation.
References
[1] Asif, M. and Muneer, T. (2007). Energy supply, its demand and security issues for developed and emerging economies. Renewable and Sustainable Energy Reviews, 11:13881413.
[2] Filippov, S.P., Dil'man M.D. and Ionov, M.S. (2017). Demand of the power industry of Russia for gas turbines: the current state and prospects. Thermal Engineering, 64:829-840.
[3] Taylor, L.D. (1975). The Demand for Electricity: A Survey. The Bell Journal of Economics, 6:74110.
[4] Gaur, K., Rathour, H.K., Agarwal, P.K., Baba K.V.S. and Soonee, S.K. Analysing the Electricity Demand Pattern. National Power Systems Conference (NPSC), Bhubaneswar, India, 2016.
[5] Zhang, X., Ramirez-Mendiola, J.L., Li M. and Guo, L. (2022). Electricity consumption pattern analysis beyond traditional clustering methods: A novel self-adapting semi-supervised clustering method and application case study. Applied Energy, 308:1-32.
[6] Derakhshan, G., Shayanfar, H.A. and Kazemi, A. (2016). The optimization of demand response programs in smart grids. Energy Policy, 94:295-306.
[7] Kan, X., Reichenberg L. and Hedenus, F. (2021). The impacts of the electricity demand pattern on electricity system cost and the electricity supply mix: A comprehensive modeling analysis for Europe. Energy, 235:1-14.
[8] Mahdavi, S.S., Saebi, J. and Ghasemi, A. (2023). Risk-based approach for self-scheduling of virtual power plants in competitive power markets. Journal of Operation and Automation in Power Engineering, 11:94-104.
[9] Fernandez, D.A.P., Foliaco, B., Padilla, R.V., Bula, A. and Quiroga, A.G. (2021). High ambient temperature effects on the performance of a gas turbine-based cogeneration system with supplementary fire in a tropical climate. Case Studies in Thermal Engineering, 26:101206101219.
[10] Emami-Mehrgani, B., Neumann, W.P., Nadeau, S. and Bazrafshan, M. (2016). Considering human error in optimizing production and corrective and preventive maintenance policies for manufacturing systems. Applied Mathematical Modelling, 40:2056-2074.
[11] Zhao, Y., Volovoi, V., Waters, M. and Mavris, D. (2006). A Sequential Approach for Gas Turbine Power Plant Preventive Maintenance Scheduling. Journal of Engineering for Gas Turbines and Power, 128:796-805.
[12] Sammour, A.A., Komarov, O.V., Qasim, M.A., Almalghouj, S., Dakkak, A.M.A. and Du, Y. (2023). Ambient conditions impact on combined cycle gas turbine power plant performance. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 45:557-574.
[13] Danish, M.S.S., Nazari, Z. and Senjyu, T. (2023). AI-coherent data-driven forecasting model for a combined cycle power plant. Energy Conversion and Management, 286:1-20.
[14] Shukla, A.K. and Singh, O. (2014). Effect of compressor inlet temperature & relative humidity on gas turbine cycle performance. International Journal of Scientific & Engineering Research, 5:664-670.
[15] Hanachi, H., Liu, J., Banerjee, A. and Chen, Y. Effects of the intake air humidity on gas turbine performance monitoring. ASME Turbo Exposition, Quebec, Canada, 2015.
[16] Yeganeh, A.H.J., Behbahaninia, A. and Ghadamabadi, P. (2022). Monte Carlo Simulation of
a Combined-Cycle Power Plant Considering Ambient Temperature Fluctuations. Journal of Power and Energy Engineering, 10:1-21.
[17] Ghaedi, A., Gorginpour, H. and Noroozi, E. (2021). Operation Studies of the Power Systems Containing Combined Heat and Power Plants. Journal of Operation and Automation in Power Engineering, 9:160-171.
[18] Singh, D. and Taneja, G. (2013). Reliability analysis of a power generating system through gas and steam turbines with scheduled inspection. Aryabhatta Journal of Mathematics and Informatics, 5:373-380.
[19] Singh, D. and Taneja, G. (2014). Reliability and economic analysis of a power generating system comprising one gas and one steam turbine with random inspection. Journal of Mathematics and Statistics, 10:436-442.
[20] Rajesh, Taneja, G. and Prasad, J. (2018). Reliability and availability analysis for a three-unit gas turbine power generating system with seasonal effect and FCFS repair pattern. International Journal of Applied Engineering Research, 13:10948-10964.
[21] Pinki and Singh, D. (2023). Reliability modeling of two-unit gas turbine system considering the effect of humidity. Reliability: Theory and Applications, 18:607-617.
[22] Singh, D. Reliability and economic analysis of some models on gas turbine power plants, Ph.D. Thesis, Maharshi Dayanand University Rohtak, 2013.
